## Regression models of placement outcomes
library(tidyverse)
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library(broom)
library(forcats)
library(rstanarm)
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## rstanarm (Version 2.18.2, packaged: 2018-11-08 22:19:38 UTC)
## - Do not expect the default priors to remain the same in future rstanarm versions.
## Thus, R scripts should specify priors explicitly, even if they are just the defaults.
## - For execution on a local, multicore CPU with excess RAM we recommend calling
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## - Plotting theme set to bayesplot::theme_default().
options(mc.cores = parallel::detectCores() - 2)
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theme_set(theme_minimal())
library(tictoc)
library(assertthat)
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## has_name
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knitr::opts_chunk$set(message = FALSE)
source('../R/predictions.R')
source('../R/posterior_estimates.R')
data_folder = '../data/'
# cluster_distances = read_csv(str_c(data_folder,
# '00_k9distances_2019-03-15.csv')) %>%
# count(cluster_lvl4 = cluster, average_distance = avgDist) %>%
# mutate(cluster_lvl4 = as.character(cluster_lvl4))
#
# ggplot(cluster_distances, aes(cluster_lvl4, scale(average_distance))) +
# geom_label(aes(label = n, fill = n, size = n), color = 'white')
load(str_c(data_folder, '01_parsed.Rdata'))
univ_df = read_rds(str_c(data_folder, '02_univ_net_stats.rds')) #%>%
# left_join(cluster_distances)
individual_df = individual_df %>%
left_join(univ_df, by = c('placing_univ_id' = 'univ_id')) %>%
## Use the canonical names from univ_df
select(-placing_univ) %>%
## Drop NAs
# filter(complete.cases(.))
filter_at(vars('permanent', 'aos_category',
'graduation_year', 'prestige',
'community', 'cluster_lvl4',
'gender', 'frac_w',
'frac_high_prestige', 'total_placements'),
all_vars(negate(is.na)(.))) %>%
mutate(perc_w = 100*frac_w,
perc_high_prestige = 100*frac_high_prestige)
## Variables to consider: aos_category; graduation_year; placement_year; prestige; out_centrality; cluster; community; placing_univ_id; gender; country; perc_w; total_placements
## Giant pairs plot/correlogram
## perc_high_prestige, out_centrality, and prestige are all tightly correlated
## All other pairs have low to moderate correlation
individual_df %>%
select(permanent, aos_category, aos_diversity, perc_high_prestige,
graduation_year, placement_year, prestige,
in_centrality, out_centrality, community,
cluster_lvl4, #average_distance,
gender, country, perc_w,
total_placements) %>%
mutate_if(negate(is.numeric), function(x) as.integer(as.factor(x))) %>%
mutate_at(vars(in_centrality, out_centrality), log10) %>%
# GGally::ggpairs()
cor() %>%
as_tibble(rownames = 'Var1') %>%
gather(key = 'Var2', value = 'cor', -Var1) %>%
ggplot(aes(Var1, Var2, fill = cor)) +
geom_tile() +
geom_text(aes(label = round(cor, digits = 2)),
color = 'white') +
scale_fill_gradient2()
## No indication that AOS diversity has any effect
ggplot(individual_df, aes(aos_diversity, 1*permanent)) +
geom_point() +
geom_smooth(method = 'loess')
## And not for fraction of PhDs awarded to women women, either
ggplot(individual_df, aes(frac_w, 1*permanent)) +
geom_point() +
geom_smooth(method = 'loess')
## Descriptive statistics ----
individual_df %>%
select(permanent, aos_category,
graduation_year, gender) %>%
gather(key = variable, value = value) %>%
count(variable, value)
## Warning: attributes are not identical across measure variables;
## they will be dropped
## # A tibble: 14 x 3
## variable value n
## <chr> <chr> <int>
## 1 aos_category History and Traditions 502
## 2 aos_category LEMM 568
## 3 aos_category Science, Logic, and Math 288
## 4 aos_category Value Theory 656
## 5 gender m 1446
## 6 gender o 1
## 7 gender w 567
## 8 graduation_year 2012 421
## 9 graduation_year 2013 414
## 10 graduation_year 2014 413
## 11 graduation_year 2015 397
## 12 graduation_year 2016 369
## 13 permanent FALSE 916
## 14 permanent TRUE 1098
individual_df %>%
select(prestige, country) %>%
gather(key = variable, value = value) %>%
count(variable, value)
## # A tibble: 10 x 3
## variable value n
## <chr> <chr> <int>
## 1 country Australia 52
## 2 country Belgium 54
## 3 country Canada 149
## 4 country France 7
## 5 country Hungary 7
## 6 country New Zealand 7
## 7 country U.K. 268
## 8 country U.S. 1470
## 9 prestige high-prestige 1036
## 10 prestige low-prestige 978
individual_df %>%
select(frac_w, total_placements, perm_placement_rate) %>%
gather(key = variable, value = value) %>%
group_by(variable) %>%
summarize_at(vars(value), funs(min, max, mean, median, sd),
na.rm = TRUE)
## Warning: funs() is soft deprecated as of dplyr 0.8.0
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## # A tibble: 3 x 6
## variable min max mean median sd
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 frac_w 0 0.667 0.277 0.265 0.138
## 2 perm_placement_rate 0.133 1 0.533 0.538 0.170
## 3 total_placements 2 73 24.0 21 14.4
## Model -----
model_file = str_c(data_folder, '03_model.Rds')
if (!file.exists(model_file)) {
## ~700 seconds
tic()
model = individual_df %>%
mutate(prestige = fct_relevel(prestige, 'low-prestige'),
country = fct_relevel(country, 'U.S.')) %>%
stan_glmer(formula = permanent ~
(1|aos_category) +
gender +
(1|graduation_year) +
(1|placement_year) +
1 +
aos_diversity +
(1|community) +
(1|cluster_lvl4) +
# average_distance +
log10(in_centrality) +
total_placements +
perc_w +
country +
prestige,
family = 'binomial',
## Priors
## Constant and coefficients
prior_intercept = normal(0, .5), ## constant term + random intercepts
prior = normal(0, .5),
## error sd
prior_aux = exponential(rate = 1,
autoscale = TRUE),
## random effects covariance
prior_covariance = decov(regularization = 1,
concentration = 1,
shape = 1, scale = 1),
seed = 1159518215,
adapt_delta = .99,
chains = 4, iter = 4000)
toc()
write_rds(model, model_file)
} else {
model = read_rds(model_file)
}
## Check ESS and Rhat
## Rhats all look good. ESS a little low for grad years + some sigmas
model %>%
summary() %>%
as.data.frame() %>%
rownames_to_column('parameter') %>%
select(parameter, n_eff, Rhat) %>%
# knitr::kable()
ggplot(aes(n_eff, Rhat, label = parameter)) +
geom_point() +
geom_vline(xintercept = 4000) +
geom_hline(yintercept = 1.01)
if (require(plotly)) {
plotly::ggplotly()
}
## Variables w/ fewer than 3000 effective draws
## covariance on random intercepts; log posterior
model %>%
summary() %>%
as.data.frame() %>%
rownames_to_column('parameter') %>%
as_tibble() %>%
filter(n_eff < 3000) %>%
select(parameter, n_eff)
## # A tibble: 2 x 2
## parameter n_eff
## <chr> <dbl>
## 1 Sigma[community:(Intercept),(Intercept)] 1968
## 2 log-posterior 1710
## Check predictions
pp_check(model, nreps = 200)
pp_check(model, nreps = 200, plotfun = 'ppc_bars')
## <https://arxiv.org/pdf/1605.01311.pdf>
pp_check(model, nreps = 200, plotfun = 'ppc_rootogram')
pp_check(model, nreps = 200, plotfun = 'ppc_rootogram', style = 'hanging')
estimates = posterior_estimates(model)
estimates %>%
filter(entity != 'intercept',
group != 'placement_year') %>%
mutate_if(is.numeric, ~ . - 1) %>%
ggplot(aes(x = level, y = estimate,
ymin = lower, ymax = upper,
color = group)) +
geom_hline(yintercept = 0, linetype = 'dashed') +
geom_pointrange() +
scale_color_viridis_d(name = 'covariate\ngroup') +
xlab('') + #ylab('') +
scale_y_continuous(labels = scales::percent_format(),
name = '') +
coord_flip() +
facet_wrap(~ entity, scales = 'free')
ggsave('../plots/03_estimates.png',
width = 10, height = 5,
scale = 1.5)
sessionInfo()
## R version 3.5.1 (2018-07-02)
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## [1] stats graphics grDevices utils datasets methods base
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